Abumalloh Rabab Ali, Nilashi Mehrbakhsh, Samad Sarminah, Ahmadi Hossein, Alghamdi Abdullah, Alrizq Mesfer, Alyami Sultan
Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Computer Science, Duy Tan University, Da Nang, Vietnam; UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang 11800, Malaysia.
Ageing Res Rev. 2024 Apr;96:102285. doi: 10.1016/j.arr.2024.102285. Epub 2024 Mar 28.
Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
帕金森病（PD）是一种由多巴胺分泌减少引发的进行性神经退行性疾病。深度学习（DL）在PD诊断研究中受到了广泛关注，该领域发表的论文数量不断增加。与普通机器学习方法相比，使用DL进行PD检测展现出了更具前景的结果。本文旨在对该领域的显著进展进行文献计量分析和文献综述。为实现研究目标，我们检索并分析了Scopus数据库中的现有研究论文。随后，我们进行了文献计量分析，通过使用VOSviewer软件提供文献计量数据的可视化表示，来审视被调查研究中的关键词、作者和国家的结构。该研究还对文献进行了深入综述，重点关注PD的不同指标、所采用的方法以及性能指标。结果表明，随着时间的推移，使用DL方法进行PD诊断取得了稳步发展，并且全球范围内的研究具有很大的多样性。此外，文献综述还指出了DL方法在增量学习方面，特别是在大数据分析方面存在的研究空白。
